------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\jar68\OneDrive\Ongoing Work\Party Cues and Suspicion Paper\Final Datavserse\Figure4\figure4_tables_ob4to6.log
  log type:  text
 opened on:  23 Jun 2021, 15:19:27

. 
. /*******************************
> Table OB4
> ******************************/
. 
. use "pooled_argdiff.dta"

. set more off

. 
. /********Some Cleaning***********/
. label var proarg "Pro Argument" 

. label var conarg "Con Argument"

. label var argdiff "Pro - Con" 

. label var treat "Treatment"

. label var polissue "Study & Issue"

. encode treat_policy , gen(trp)

. label var treat_policy "Policy Treatment"

. 
. 
. ***Diff Argument
. eststo clear

. regress argdiff i.treat i.polissue

      Source |       SS           df       MS      Number of obs   =     5,039
-------------+----------------------------------   F(6, 5032)      =     21.25
       Model |  4.43668155         6  .739446925   Prob > F        =    0.0000
    Residual |  175.113774     5,032  .034800034   R-squared       =    0.0247
-------------+----------------------------------   Adj R-squared   =    0.0235
       Total |  179.550455     5,038  .035639233   Root MSE        =    .18655

---------------------------------------------------------------------------------------------
                    argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
                      treat |
                 Party Cue  |   .0600179   .0071848     8.35   0.000     .0459325    .0741033
         Cue w/Insinuation  |   .0325512   .0065223     4.99   0.000     .0197647    .0453377
                            |
                   polissue |
Exp 2: Conservative Change  |  -.0487457   .0088583    -5.50   0.000     -.066112   -.0313795
     Exp 2: Liberal Change  |  -.0328962   .0088487    -3.72   0.000    -.0502435    -.015549
Exp 3: Conservative Change  |  -.0405443   .0080553    -5.03   0.000    -.0563363   -.0247523
     Exp 3: Liberal Change  |  -.0583174   .0080332    -7.26   0.000     -.074066   -.0425688
                            |
                      _cons |   .5168629   .0070945    72.85   0.000     .5029545    .5307712
---------------------------------------------------------------------------------------------

.         estimates store m1

.         lincom _b[2.treat] - _b[3.treat]

 ( 1)  2.treat - 3.treat = 0

------------------------------------------------------------------------------
     argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0274667   .0065431     4.20   0.000     .0146394     .040294
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .0274667

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  4.1978063

.         
. regress argdiff i.treat if exp == 1

      Source |       SS           df       MS      Number of obs   =     1,013
-------------+----------------------------------   F(2, 1010)      =     15.02
       Model |  1.11549516         2   .55774758   Prob > F        =    0.0000
    Residual |  37.5008397     1,010  .037129544   R-squared       =    0.0289
-------------+----------------------------------   Adj R-squared   =    0.0270
       Total |  38.6163349     1,012  .038158434   Root MSE        =    .19269

------------------------------------------------------------------------------------
           argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             treat |
        Party Cue  |   .0804059   .0149838     5.37   0.000      .051003    .1098088
Cue w/Insinuation  |   .0533189   .0146624     3.64   0.000     .0245466    .0820913
                   |
             _cons |   .5031863   .0104501    48.15   0.000     .4826799    .5236927
------------------------------------------------------------------------------------

.         estimates store m2

.         lincom _b[2.treat] - _b[3.treat]

 ( 1)  2.treat - 3.treat = 0

------------------------------------------------------------------------------
     argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0270869   .0148691     1.82   0.069     -.002091    .0562649
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .02708691

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  1.8216864

. 
. regress argdiff i.treat i.trp if exp == 2

      Source |       SS           df       MS      Number of obs   =     1,581
-------------+----------------------------------   F(3, 1577)      =      8.29
       Model |  .937959195         3  .312653065   Prob > F        =    0.0000
    Residual |  59.4403615     1,577  .037692049   R-squared       =    0.0155
-------------+----------------------------------   Adj R-squared   =    0.0137
       Total |  60.3783207     1,580  .038214127   Root MSE        =    .19414

------------------------------------------------------------------------------------
           argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             treat |
        Party Cue  |      .0544   .0119715     4.54   0.000     .0309182    .0778817
Cue w/Insinuation  |   .0401141   .0119491     3.36   0.001     .0166762     .063552
                   |
               trp |
   Liberal Change  |    .015778   .0097657     1.62   0.106    -.0033772    .0349331
             _cons |    .467488   .0097516    47.94   0.000     .4483606    .4866155
------------------------------------------------------------------------------------

.         estimates store m3

.         lincom _b[2.treat] - _b[3.treat]

 ( 1)  2.treat - 3.treat = 0

------------------------------------------------------------------------------
     argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0142859   .0119604     1.19   0.232    -.0091741    .0377458
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .01428586

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  1.1944291

.         
. eststo: regress argdiff i.treat i.trp if exp == 3

      Source |       SS           df       MS      Number of obs   =     2,445
-------------+----------------------------------   F(3, 2441)      =      9.28
       Model |  .889315435         3  .296438478   Prob > F        =    0.0000
    Residual |  77.9513415     2,441  .031934183   R-squared       =    0.0113
-------------+----------------------------------   Adj R-squared   =    0.0101
       Total |   78.840657     2,444  .032258861   Root MSE        =     .1787

------------------------------------------------------------------------------------
           argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             treat |
        Party Cue  |   .0521421   .0113999     4.57   0.000     .0297876    .0744966
Cue w/Insinuation  |   .0181628    .009323     1.95   0.052     -.000119    .0364446
                   |
               trp |
   Liberal Change  |   -.017538     .00723    -2.43   0.015    -.0317155   -.0033604
             _cons |   .4863881   .0087991    55.28   0.000     .4691336    .5036426
------------------------------------------------------------------------------------
(est1 stored)

.         estimates store m4

.         lincom _b[2.treat] - _b[3.treat]

 ( 1)  2.treat - 3.treat = 0

------------------------------------------------------------------------------
     argdiff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0339793   .0093143     3.65   0.000     .0157146     .052244
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .0339793

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  3.6480832

. 
. esttab m1 m2 m3 m4 using "argdiff.rtf", replace ///
> onecell label nobaselevels b(2) se star(* 0.05 ** 0.01 *** 0.001) ///
>         mtitles("Pooled" "Exp. 1" "Exp. 2" "Exp. 3") ///
>         title("{\b Table OB4:} Argument Polarization (Pro/In - Con/Out)") ///
>         stats(N diff sig) 
(note: file argdiff.rtf not found)
(output written to argdiff.rtf)

.         
. /*******************************
> Table OB5
> ******************************/
. 
. clear

. use "prox_data.dta"

. set more off

. 
. eststo clear

. regress prox01 i.treat_3 i.stereo 

      Source |       SS           df       MS      Number of obs   =     2,455
-------------+----------------------------------   F(3, 2451)      =     15.39
       Model |  3.22862252         3  1.07620751   Prob > F        =    0.0000
    Residual |  171.366082     2,451  .069916802   R-squared       =    0.0185
-------------+----------------------------------   Adj R-squared   =    0.0173
       Total |  174.594705     2,454  .071146986   Root MSE        =    .26442

------------------------------------------------------------------------------------
            prox01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
           treat_3 |
        Party Cue  |   .0687061   .0168265     4.08   0.000     .0357104    .1017018
Cue w/Insinuation  |   .0317535   .0137653     2.31   0.021     .0047607    .0587464
                   |
            stereo |
    Stereotypical  |   .0586125   .0106767     5.49   0.000     .0376761    .0795489
             _cons |   .6509425   .0130461    49.90   0.000     .6253599    .6765251
------------------------------------------------------------------------------------

.         estimates store m1

.         lincom _b[2.treat] - _b[3.treat]

 ( 1)  2.treat_3 - 3.treat_3 = 0

------------------------------------------------------------------------------
      prox01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0369526   .0137456     2.69   0.007     .0099985    .0639067
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .03695256

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  2.6883274

. 
. /*****Table****/
. esttab m1 using "prox_table.rtf", replace ///
> onecell label nobaselevels b(2) se star(* 0.05 ** 0.01 *** 0.001) ///
>         mtitles("Overall" "State Growth" "Pers. Finances" "Community" "Unemployment" "Poor" "Middle Class" "Rich") ///
>         title("{\b Table OB5:} Subjective Proximity, Experiment 3") ///
>         stats(N diff sig) 
(note: file prox_table.rtf not found)
(output written to prox_table.rtf)

. 
. 
. /*******************************
> Table OB6
> ******************************/
. 
. clear

. use "inferences_data.dta"

. set more off

. 
. foreach var in  state_growth pers_finances community unemployment poor middleclass rich { 
  2.         summ `var' 
  3.         replace `var' = (`var' - r(min))/(r(max)-r(min))
  4. }

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
state_growth |      2,451    4.142391    1.620444          1          7
variable state_growth was long now double
(2,451 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
pers_finan~s |      2,453    3.959234    1.583449          1          7
variable pers_finances was long now double
(2,453 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
   community |      2,453    4.015084    1.577581          1          7
variable community was long now double
(2,453 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
unemployment |      2,452    3.997145    1.507086          1          7
variable unemployment was long now double
(2,452 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        poor |      2,448    3.895833    1.825492          1          7
variable poor was long now double
(2,448 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
 middleclass |      2,451    3.994696    1.598588          1          7
variable middleclass was long now double
(2,451 real changes made)

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
        rich |      2,449    4.372397    1.620232          1          7
variable rich was long now double
(2,449 real changes made)

. 
. /******Models*****/
. eststo clear

. regress inferences i.treat i.stereo 

      Source |       SS           df       MS      Number of obs   =     2,460
-------------+----------------------------------   F(3, 2456)      =     11.37
       Model |  1.38598013         3  .461993375   Prob > F        =    0.0000
    Residual |  99.7897888     2,456  .040631022   R-squared       =    0.0137
-------------+----------------------------------   Adj R-squared   =    0.0125
       Total |  101.175769     2,459  .041145087   Root MSE        =    .20157

------------------------------------------------------------------------------------
        inferences |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             treat |
        Party Cue  |   .0401107   .0128272     3.13   0.002     .0149574     .065264
Cue w/Insinuation  |   .0270224   .0104891     2.58   0.010     .0064539    .0475908
                   |
            stereo |
    Stereotypical  |   .0398809   .0081309     4.90   0.000     .0239369     .055825
             _cons |   .4653587   .0099437    46.80   0.000     .4458598    .4848575
------------------------------------------------------------------------------------

.         estimates store m1

.         lincom _b[2.treat] - _b[3.treat]

 ( 1)  2.treat - 3.treat = 0

------------------------------------------------------------------------------
  inferences |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0130883    .010474     1.25   0.212    -.0074505    .0336271
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .01308832

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  1.2495998

. 
. regress state_growth i.treat i.stereo 

      Source |       SS           df       MS      Number of obs   =     2,451
-------------+----------------------------------   F(3, 2447)      =     14.77
       Model |   3.1776979         3  1.05923263   Prob > F        =    0.0000
    Residual |  175.525235     2,447  .071730787   R-squared       =    0.0178
-------------+----------------------------------   Adj R-squared   =    0.0166
       Total |  178.702933     2,450  .072939973   Root MSE        =    .26783

------------------------------------------------------------------------------------
      state_growth |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             treat |
        Party Cue  |   .0657544   .0170946     3.85   0.000      .032233    .0992759
Cue w/Insinuation  |   .0425376   .0139828     3.04   0.002     .0151182     .069957
                   |
            stereo |
    Stereotypical  |   .0584645    .010823     5.40   0.000     .0372412    .0796877
             _cons |   .4564109    .013243    34.46   0.000     .4304422    .4823796
------------------------------------------------------------------------------------

.         estimates store m2

.         lincom _b[2.treat] - _b[3.treat]

 ( 1)  2.treat - 3.treat = 0

------------------------------------------------------------------------------
state_growth |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0232169   .0139411     1.67   0.096    -.0041207    .0505544
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .02321687

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  1.6653553

. 
. regress pers_finances i.treat i.stereo 

      Source |       SS           df       MS      Number of obs   =     2,453
-------------+----------------------------------   F(3, 2449)      =      8.55
       Model |  1.76938813         3  .589796044   Prob > F        =    0.0000
    Residual |  169.006261     2,449  .069010315   R-squared       =    0.0104
-------------+----------------------------------   Adj R-squared   =    0.0091
       Total |  170.775649     2,452  .069647491   Root MSE        =     .2627

------------------------------------------------------------------------------------
     pers_finances |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             treat |
        Party Cue  |   .0507745   .0167422     3.03   0.002     .0179441    .0836049
Cue w/Insinuation  |     .03679   .0137058     2.68   0.007     .0099139    .0636662
                   |
            stereo |
    Stereotypical  |   .0419402   .0106118     3.95   0.000     .0211312    .0627491
             _cons |   .4404256    .012982    33.93   0.000     .4149688    .4658824
------------------------------------------------------------------------------------

.         estimates store m3

.         lincom _b[2.treat] - _b[3.treat]

 ( 1)  2.treat - 3.treat = 0

------------------------------------------------------------------------------
pers_finan~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0139845   .0136549     1.02   0.306    -.0127919    .0407609
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .0139845

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  1.024135

. 
. regress community i.treat i.stereo 

      Source |       SS           df       MS      Number of obs   =     2,453
-------------+----------------------------------   F(3, 2449)      =      8.93
       Model |  1.83394281         3   .61131427   Prob > F        =    0.0000
    Residual |  167.678332     2,449  .068468082   R-squared       =    0.0108
-------------+----------------------------------   Adj R-squared   =    0.0096
       Total |  169.512275     2,452  .069132249   Root MSE        =    .26166

------------------------------------------------------------------------------------
         community |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             treat |
        Party Cue  |   .0383503   .0166932     2.30   0.022     .0056162    .0710845
Cue w/Insinuation  |   .0253698   .0136495     1.86   0.063     -.001396    .0521356
                   |
            stereo |
    Stereotypical  |   .0489316     .01057     4.63   0.000     .0282045    .0696587
             _cons |   .4556659   .0129309    35.24   0.000     .4303093    .4810225
------------------------------------------------------------------------------------

.         estimates store m4

.         lincom _b[2.treat] - _b[3.treat]

 ( 1)  2.treat - 3.treat = 0

------------------------------------------------------------------------------
   community |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0129806   .0136195     0.95   0.341    -.0137263    .0396875
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .01298056

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  .95308698

.         
. regress unemployment i.treat i.stereo 

      Source |       SS           df       MS      Number of obs   =     2,452
-------------+----------------------------------   F(3, 2448)      =     10.62
       Model |  1.98635959         3  .662119864   Prob > F        =    0.0000
    Residual |  152.651974     2,448  .062357833   R-squared       =    0.0128
-------------+----------------------------------   Adj R-squared   =    0.0116
       Total |  154.638334     2,451  .063091935   Root MSE        =    .24972

------------------------------------------------------------------------------------
      unemployment |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             treat |
        Party Cue  |    .047685   .0158988     3.00   0.003     .0165085    .0788615
Cue w/Insinuation  |   .0415671   .0130022     3.20   0.001     .0160706    .0670637
                   |
            stereo |
    Stereotypical  |   .0453856   .0100893     4.50   0.000      .025601    .0651701
             _cons |   .4428705    .012322    35.94   0.000     .4187078    .4670331
------------------------------------------------------------------------------------

.         estimates store m5

.         lincom _b[2.treat] - _b[3.treat]

 ( 1)  2.treat - 3.treat = 0

------------------------------------------------------------------------------
unemployment |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0061178    .012993     0.47   0.638    -.0193606    .0315963
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .00611783

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  .47085508

. 
. regress poor i.treat i.stereo 

      Source |       SS           df       MS      Number of obs   =     2,448
-------------+----------------------------------   F(3, 2444)      =      5.89
       Model |  1.62608893         3  .542029645   Prob > F        =    0.0005
    Residual |  224.886064     2,444  .092015574   R-squared       =    0.0072
-------------+----------------------------------   Adj R-squared   =    0.0060
       Total |  226.512153     2,447  .092567288   Root MSE        =    .30334

------------------------------------------------------------------------------------
              poor |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             treat |
        Party Cue  |   .0416868   .0193227     2.16   0.031     .0037961    .0795774
Cue w/Insinuation  |   .0421098   .0158225     2.66   0.008     .0110828    .0731367
                   |
            stereo |
    Stereotypical  |   .0393755   .0122668     3.21   0.001     .0153211    .0634299
             _cons |   .4298555   .0149855    28.68   0.000     .4004699     .459241
------------------------------------------------------------------------------------

.         estimates store m6

.         lincom _b[2.treat] - _b[3.treat]

 ( 1)  2.treat - 3.treat = 0

------------------------------------------------------------------------------
        poor |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   -.000423   .0157753    -0.03   0.979    -.0313572    .0305113
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  -.00042297

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  -.02681231

. 
. regress middleclass i.treat i.stereo 

      Source |       SS           df       MS      Number of obs   =     2,451
-------------+----------------------------------   F(3, 2447)      =     11.55
       Model |  2.42842868         3  .809476228   Prob > F        =    0.0000
    Residual |  171.486323     2,447   .07008023   R-squared       =    0.0140
-------------+----------------------------------   Adj R-squared   =    0.0128
       Total |  173.914751     2,450  .070985613   Root MSE        =    .26473

------------------------------------------------------------------------------------
       middleclass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             treat |
        Party Cue  |    .061066   .0168633     3.62   0.000     .0279983    .0941338
Cue w/Insinuation  |    .038201   .0137943     2.77   0.006     .0111513    .0652506
                   |
            stereo |
    Stereotypical  |    .049641   .0106985     4.64   0.000      .028662      .07062
             _cons |   .4397113   .0130699    33.64   0.000     .4140821    .4653405
------------------------------------------------------------------------------------

.         estimates store m7

.         lincom _b[2.treat] - _b[3.treat]

 ( 1)  2.treat - 3.treat = 0

------------------------------------------------------------------------------
 middleclass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0228651   .0137743     1.66   0.097    -.0041455    .0498757
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .02286508

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  1.659976

. 
. regress rich i.treat i.stereo 

      Source |       SS           df       MS      Number of obs   =     2,449
-------------+----------------------------------   F(3, 2445)      =      1.72
       Model |  .375109802         3  .125036601   Prob > F        =    0.1615
    Residual |   178.13528     2,445  .072856965   R-squared       =    0.0021
-------------+----------------------------------   Adj R-squared   =    0.0009
       Total |   178.51039     2,448  .072920911   Root MSE        =    .26992

------------------------------------------------------------------------------------
              rich |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
             treat |
        Party Cue  |  -.0191851   .0172378    -1.11   0.266    -.0529873     .014617
Cue w/Insinuation  |  -.0310859   .0141152    -2.20   0.028    -.0587648    -.003407
                   |
            stereo |
    Stereotypical  |  -.0050001   .0109123    -0.46   0.647    -.0263985    .0163982
             _cons |   .5870158   .0133794    43.87   0.000     .5607797    .6132519
------------------------------------------------------------------------------------

.         estimates store m8

.         lincom _b[2.treat] - _b[3.treat]

 ( 1)  2.treat - 3.treat = 0

------------------------------------------------------------------------------
        rich |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0119008   .0140407     0.85   0.397    -.0156322    .0394338
------------------------------------------------------------------------------

.         estadd scalar diff = r(estimate)

added scalar:
               e(diff) =  .0119008

.         estadd scalar sig = r(t)

added scalar:
                e(sig) =  .84759074

. 
. /*****Table****/
. esttab m1 m2 m3 m4 m5 m6 m7 m8 using "inferences.rtf", replace ///
> onecell label nobaselevels b(2) se star(* 0.05 ** 0.01 *** 0.001) ///
>         mtitles("Overall" "State Growth" "Pers. Finances" "Community" "Unemployment" "Poor" "Middle Class" "Rich") ///
>         title("{\b Table OB6:} Inferences, Experiment 3") ///
>         stats(N diff sig) 
(note: file inferences.rtf not found)
(output written to inferences.rtf)

. 
. log close
      name:  <unnamed>
       log:  C:\Users\jar68\OneDrive\Ongoing Work\Party Cues and Suspicion Paper\Final Datavserse\Figure4\figure4_tables_ob4to6.log
  log type:  text
 closed on:  23 Jun 2021, 15:19:28
------------------------------------------------------------------------------------------------------------------------------------------------------------------------
